Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Numeric10
Categorical1

Warnings

target is uniformly distributed Uniform
X0 has unique values Unique
X1 has unique values Unique
X2 has unique values Unique
X3 has unique values Unique
X4 has unique values Unique
X5 has unique values Unique
X6 has unique values Unique
X7 has unique values Unique
X8 has unique values Unique
X9 has unique values Unique

Reproduction

Analysis started2021-02-12 23:27:17.500694
Analysis finished2021-02-12 23:39:55.661565
Duration12 minutes and 38.16 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

X0
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.02783488251
Minimum-3.725393248
Maximum3.215204309
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:40:04.147396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.725393248
5-th percentile-1.662647289
Q1-0.7401549003
median-0.0221044273
Q30.662496976
95-th percentile1.642950136
Maximum3.215204309
Range6.940597557
Interquartile range (IQR)1.402651876

Descriptive statistics

Standard deviation1.034847029
Coefficient of variation (CV)-37.17806347
Kurtosis0.04127598979
Mean-0.02783488251
Median Absolute Deviation (MAD)0.7049282732
Skewness0.0598836368
Sum-27.83488251
Variance1.070908373
MonotocityNot monotonic
2021-02-12T18:40:13.240161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54348044271
 
0.1%
0.52126364611
 
0.1%
-1.295760151
 
0.1%
-0.77060323521
 
0.1%
-1.624910681
 
0.1%
1.0421731691
 
0.1%
0.0238770861
 
0.1%
-0.17574640351
 
0.1%
-0.37448247951
 
0.1%
0.82951435561
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.7253932481
0.1%
-3.2218176111
0.1%
-2.9936855021
0.1%
-2.8741094121
0.1%
-2.8726589341
0.1%
-2.8277039531
0.1%
-2.5499039351
0.1%
-2.5021661811
0.1%
-2.4510019381
0.1%
-2.446628611
0.1%
ValueCountFrequency (%)
3.2152043091
0.1%
3.2003994521
0.1%
2.8825467011
0.1%
2.7430969261
0.1%
2.7430136921
0.1%
2.7422499241
0.1%
2.5733880011
0.1%
2.5089089041
0.1%
2.5068681231
0.1%
2.4992513711
0.1%

X1
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01876265182
Minimum-2.868279784
Maximum3.289140633
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:40:22.367474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.868279784
5-th percentile-1.685602549
Q1-0.6610106303
median-0.02024477948
Q30.6672490383
95-th percentile1.621142069
Maximum3.289140633
Range6.157420416
Interquartile range (IQR)1.328259669

Descriptive statistics

Standard deviation0.9917442857
Coefficient of variation (CV)-52.85736233
Kurtosis-0.1058802719
Mean-0.01876265182
Median Absolute Deviation (MAD)0.6708297683
Skewness0.05691931555
Sum-18.76265182
Variance0.9835567282
MonotocityNot monotonic
2021-02-12T18:40:30.850533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.30377088751
 
0.1%
-1.1021828691
 
0.1%
-0.097198783191
 
0.1%
-1.5816133091
 
0.1%
-1.5385554421
 
0.1%
-2.1102352061
 
0.1%
-0.64607433591
 
0.1%
1.8602723951
 
0.1%
-0.42812639151
 
0.1%
0.39344446011
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.8682797841
0.1%
-2.8582372581
0.1%
-2.8233249341
0.1%
-2.4217604481
0.1%
-2.3959151271
0.1%
-2.3740314651
0.1%
-2.3503443291
0.1%
-2.1851265761
0.1%
-2.171982181
0.1%
-2.1102352061
0.1%
ValueCountFrequency (%)
3.2891406331
0.1%
2.9850731081
0.1%
2.8127266741
0.1%
2.7715308431
0.1%
2.7486646511
0.1%
2.536052321
0.1%
2.5191280951
0.1%
2.4894627151
0.1%
2.4125456011
0.1%
2.3848421331
0.1%

X2
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01756611716
Minimum-2.771563382
Maximum3.263118637
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:40:39.131229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.771563382
5-th percentile-1.699184672
Q1-0.7356234251
median0.006164450888
Q30.6918092569
95-th percentile1.653727832
Maximum3.263118637
Range6.03468202
Interquartile range (IQR)1.427432682

Descriptive statistics

Standard deviation0.991531233
Coefficient of variation (CV)-56.44566889
Kurtosis-0.2207370719
Mean-0.01756611716
Median Absolute Deviation (MAD)0.7128089978
Skewness0.01713350735
Sum-17.56611716
Variance0.983134186
MonotocityNot monotonic
2021-02-12T18:40:47.454521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.330485661
 
0.1%
0.34516963111
 
0.1%
-1.3211140951
 
0.1%
-0.38477694581
 
0.1%
0.043318925721
 
0.1%
1.0720024531
 
0.1%
0.80965417951
 
0.1%
-2.0232323471
 
0.1%
-1.0018721811
 
0.1%
-0.72994271561
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.7715633821
0.1%
-2.6904878951
0.1%
-2.6870987721
0.1%
-2.4363537391
0.1%
-2.4255480431
0.1%
-2.4150777121
0.1%
-2.3687598441
0.1%
-2.3345914781
0.1%
-2.2645997791
0.1%
-2.2642454721
0.1%
ValueCountFrequency (%)
3.2631186371
0.1%
3.0503596431
0.1%
2.5594443031
0.1%
2.4474615631
0.1%
2.367479141
0.1%
2.3429649441
0.1%
2.3375718241
0.1%
2.2951751151
0.1%
2.257506981
0.1%
2.2470218781
0.1%

X3
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00850507651
Minimum-3.466020519
Maximum2.695978856
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:40:55.726718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.466020519
5-th percentile-1.615712433
Q1-0.6911398208
median-0.005168239299
Q30.6510812772
95-th percentile1.688696262
Maximum2.695978856
Range6.161999375
Interquartile range (IQR)1.342221098

Descriptive statistics

Standard deviation0.9843543746
Coefficient of variation (CV)-115.7372745
Kurtosis0.05100159467
Mean-0.00850507651
Median Absolute Deviation (MAD)0.6723187112
Skewness-0.006744751575
Sum-8.50507651
Variance0.9689535347
MonotocityNot monotonic
2021-02-12T18:41:03.888144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2968193311
 
0.1%
0.10060943531
 
0.1%
-1.2036982491
 
0.1%
0.72702927061
 
0.1%
-0.89414383471
 
0.1%
0.24761584421
 
0.1%
-0.85444683431
 
0.1%
-0.60370688161
 
0.1%
-0.94123209751
 
0.1%
0.043141392431
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.4660205191
0.1%
-3.1981079551
0.1%
-3.1030692081
0.1%
-2.6045251211
0.1%
-2.4996769411
0.1%
-2.4465245771
0.1%
-2.3905672571
0.1%
-2.3673854131
0.1%
-2.3666445011
0.1%
-2.3631464161
0.1%
ValueCountFrequency (%)
2.6959788561
0.1%
2.692944521
0.1%
2.6444123421
0.1%
2.6287457621
0.1%
2.5259050581
0.1%
2.2955697321
0.1%
2.2870050081
0.1%
2.273249621
0.1%
2.2728574281
0.1%
2.2688474631
0.1%

X4
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.940261104 × 105
Minimum-3.013417354
Maximum3.207830463
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:41:11.993969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.013417354
5-th percentile-1.583657818
Q1-0.6866717168
median0.01436524964
Q30.6302407121
95-th percentile1.588843958
Maximum3.207830463
Range6.221247816
Interquartile range (IQR)1.316912429

Descriptive statistics

Standard deviation0.9700006332
Coefficient of variation (CV)13976.42853
Kurtosis-0.08450253482
Mean6.940261104 × 105
Median Absolute Deviation (MAD)0.656487288
Skewness0.06496053177
Sum0.06940261104
Variance0.9409012285
MonotocityNot monotonic
2021-02-12T18:41:19.764812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.29579697471
 
0.1%
0.6165729121
 
0.1%
1.8910909141
 
0.1%
0.45204189061
 
0.1%
-0.59159307921
 
0.1%
-0.076419554961
 
0.1%
-0.50314441361
 
0.1%
-1.505743221
 
0.1%
-0.75002420011
 
0.1%
-0.30024124211
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.0134173541
0.1%
-2.8603554951
0.1%
-2.6910512461
0.1%
-2.6304290951
0.1%
-2.3613718171
0.1%
-2.2814279491
0.1%
-2.2524674091
0.1%
-2.204517361
0.1%
-2.1476385051
0.1%
-2.1456852051
0.1%
ValueCountFrequency (%)
3.2078304631
0.1%
3.0957365891
0.1%
2.8867408651
0.1%
2.7412368681
0.1%
2.5079732491
0.1%
2.4714701751
0.1%
2.448753721
0.1%
2.4262662921
0.1%
2.4131920131
0.1%
2.4007413941
0.1%

X5
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.08685267595
Minimum-3.079112187
Maximum2.978478811
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:41:27.571647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.079112187
5-th percentile-1.782821712
Q1-0.778828798
median-0.1029280036
Q30.6109148798
95-th percentile1.588831449
Maximum2.978478811
Range6.057590998
Interquartile range (IQR)1.389743678

Descriptive statistics

Standard deviation1.01408387
Coefficient of variation (CV)-11.67590818
Kurtosis-0.1443170436
Mean-0.08685267595
Median Absolute Deviation (MAD)0.6910856418
Skewness0.01828882805
Sum-86.85267595
Variance1.028366095
MonotocityNot monotonic
2021-02-12T18:41:35.451287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.1628415941
 
0.1%
-0.3972635481
 
0.1%
0.81609534391
 
0.1%
-0.81064586761
 
0.1%
-1.2332102281
 
0.1%
0.13637184221
 
0.1%
-1.4335940311
 
0.1%
0.0017182208131
 
0.1%
-0.13786907061
 
0.1%
0.40179628861
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.0791121871
0.1%
-3.0550248531
0.1%
-2.7255643431
0.1%
-2.6044976281
0.1%
-2.5694336121
0.1%
-2.5521479371
0.1%
-2.4807470531
0.1%
-2.4709760581
0.1%
-2.3816921571
0.1%
-2.344760751
0.1%
ValueCountFrequency (%)
2.9784788111
0.1%
2.7952855711
0.1%
2.6394593791
0.1%
2.6348067051
0.1%
2.6209865621
0.1%
2.5502385431
0.1%
2.5202373521
0.1%
2.4577010981
0.1%
2.400271841
0.1%
2.3816244031
0.1%

X6
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04701887468
Minimum-2.689934255
Maximum3.068767252
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:41:43.459006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.689934255
5-th percentile-1.631909828
Q1-0.7049966768
median-0.0787988957
Q30.6660182038
95-th percentile1.476717426
Maximum3.068767252
Range5.758701507
Interquartile range (IQR)1.371014881

Descriptive statistics

Standard deviation0.9645496408
Coefficient of variation (CV)-20.51409455
Kurtosis-0.241993075
Mean-0.04701887468
Median Absolute Deviation (MAD)0.6745001927
Skewness-0.03100341944
Sum-47.01887468
Variance0.9303560095
MonotocityNot monotonic
2021-02-12T18:41:52.590523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.2694424161
 
0.1%
-0.42180280241
 
0.1%
1.0131634511
 
0.1%
-1.7079306341
 
0.1%
0.065846594081
 
0.1%
-1.093321511
 
0.1%
-1.5437042071
 
0.1%
0.56228697641
 
0.1%
-1.1467339651
 
0.1%
0.3703503021
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.6899342551
0.1%
-2.6131651431
0.1%
-2.5767435981
0.1%
-2.5176726981
0.1%
-2.4929122271
0.1%
-2.4589599671
0.1%
-2.4412788711
0.1%
-2.4265999761
0.1%
-2.3851121491
0.1%
-2.3081795481
0.1%
ValueCountFrequency (%)
3.0687672521
0.1%
2.8609610771
0.1%
2.5481102591
0.1%
2.4911338281
0.1%
2.4519968671
0.1%
2.3273261051
0.1%
2.3074370421
0.1%
2.22483091
0.1%
2.199994841
0.1%
2.1198717691
0.1%

X7
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03374036619
Minimum-3.527814487
Maximum3.008764644
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:42:00.806503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.527814487
5-th percentile-1.606318245
Q1-0.601386081
median-0.007175449025
Q30.6985353793
95-th percentile1.667303159
Maximum3.008764644
Range6.536579132
Interquartile range (IQR)1.29992146

Descriptive statistics

Standard deviation0.9904366474
Coefficient of variation (CV)29.35465021
Kurtosis0.1487503467
Mean0.03374036619
Median Absolute Deviation (MAD)0.6496545592
Skewness-0.02257778931
Sum33.74036619
Variance0.9809647525
MonotocityNot monotonic
2021-02-12T18:42:08.757079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17891087751
 
0.1%
-0.38175247081
 
0.1%
-0.007271056461
 
0.1%
0.17216850141
 
0.1%
-1.5403790231
 
0.1%
1.2888700081
 
0.1%
-0.11743679691
 
0.1%
0.28679489451
 
0.1%
-0.90293203911
 
0.1%
1.6821431621
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.5278144871
0.1%
-2.985921321
0.1%
-2.9103884511
0.1%
-2.856902831
0.1%
-2.7126636291
0.1%
-2.7120447891
0.1%
-2.5821144781
0.1%
-2.5016173821
0.1%
-2.4484683251
0.1%
-2.4012459181
0.1%
ValueCountFrequency (%)
3.0087646441
0.1%
2.8505025451
0.1%
2.7458039751
0.1%
2.7123270921
0.1%
2.6986427011
0.1%
2.6850800961
0.1%
2.6303823191
0.1%
2.5398706941
0.1%
2.4924343481
0.1%
2.4429694721
0.1%

X8
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01454695434
Minimum-2.814727256
Maximum2.880309262
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:42:16.656816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.814727256
5-th percentile-1.701970946
Q1-0.6432174002
median0.02629163822
Q30.7131207538
95-th percentile1.633809979
Maximum2.880309262
Range5.695036518
Interquartile range (IQR)1.356338154

Descriptive statistics

Standard deviation0.9985791109
Coefficient of variation (CV)68.64523579
Kurtosis-0.1277959434
Mean0.01454695434
Median Absolute Deviation (MAD)0.6768587695
Skewness-0.04528194027
Sum14.54695434
Variance0.9971602406
MonotocityNot monotonic
2021-02-12T18:42:24.372871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.9972365841
 
0.1%
-1.7161687381
 
0.1%
-0.4074429921
 
0.1%
0.62772262521
 
0.1%
-1.7019553961
 
0.1%
-0.081607130011
 
0.1%
0.3205493121
 
0.1%
0.57510235511
 
0.1%
1.6260650031
 
0.1%
0.1320042761
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.8147272561
0.1%
-2.648878921
0.1%
-2.5982751691
0.1%
-2.5409937161
0.1%
-2.5285406491
0.1%
-2.498609341
0.1%
-2.491384451
0.1%
-2.4640035121
0.1%
-2.4624900431
0.1%
-2.3740211421
0.1%
ValueCountFrequency (%)
2.8803092621
0.1%
2.8210385161
0.1%
2.796352421
0.1%
2.7478058291
0.1%
2.7300027181
0.1%
2.6375598691
0.1%
2.4371511341
0.1%
2.3983638851
0.1%
2.362571031
0.1%
2.299925521
0.1%

X9
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.06102644479
Minimum-3.002260976
Maximum3.128679635
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T18:42:31.945126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.002260976
5-th percentile-1.697224063
Q1-0.7458677151
median-0.08633730011
Q30.6088848634
95-th percentile1.651120466
Maximum3.128679635
Range6.130940611
Interquartile range (IQR)1.354752579

Descriptive statistics

Standard deviation0.9863718346
Coefficient of variation (CV)-16.16302306
Kurtosis-0.1660889484
Mean-0.06102644479
Median Absolute Deviation (MAD)0.670333147
Skewness0.1030784375
Sum-61.02644479
Variance0.9729293961
MonotocityNot monotonic
2021-02-12T18:42:39.569824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.1469590331
 
0.1%
0.85943849921
 
0.1%
-1.3599496071
 
0.1%
-0.38468327561
 
0.1%
0.84393009281
 
0.1%
0.1458310751
 
0.1%
-0.031401464131
 
0.1%
-1.1284101821
 
0.1%
-0.45362882811
 
0.1%
0.097258207251
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.0022609761
0.1%
-2.816712931
0.1%
-2.6731562721
0.1%
-2.5712225111
0.1%
-2.4104745881
0.1%
-2.3708578761
0.1%
-2.3705112551
0.1%
-2.3218264671
0.1%
-2.289012431
0.1%
-2.23744771
0.1%
ValueCountFrequency (%)
3.1286796351
0.1%
2.8707636911
0.1%
2.5730670231
0.1%
2.4546170781
0.1%
2.4412889031
0.1%
2.3699553331
0.1%
2.2935360661
0.1%
2.2028084361
0.1%
2.1648299821
0.1%
2.1496629091
0.1%

target
Categorical

UNIFORM

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
500 
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
0500
50.0%
1500
50.0%
2021-02-12T18:42:55.067616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T18:43:02.952816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring characters

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Interactions

2021-02-12T18:27:27.144441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:27:35.427865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:27:43.788130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:27:52.038658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:28:00.311429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:28:08.299511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:28:16.180019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:28:24.423915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:28:32.376337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:28:40.318813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:28:48.132652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:28:56.131988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:29:04.239226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:29:12.135960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:29:20.762917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:29:28.934574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:29:37.228219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:29:45.423489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:29:53.467961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:30:01.563167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:30:09.332253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:30:17.119948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:30:25.221902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:30:33.064732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:30:41.324350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:30:49.384420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:30:57.782500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:31:05.671750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:31:13.493448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:31:21.483542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:31:29.878433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:31:38.237211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:31:46.183926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:31:54.054636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:32:02.075310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:32:10.272671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:32:18.102351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:32:26.531092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:32:35.029600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:32:43.135618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:32:51.234712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:32:59.355792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:33:07.647938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:33:15.402868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:33:23.256088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:33:31.422578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:33:39.782858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:33:47.861256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:33:56.058442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:34:04.087494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:34:12.248019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:34:20.423295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:34:28.547316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:34:36.976193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:34:45.259943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:34:53.687953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:35:02.718172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:35:10.820221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:35:18.512283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:35:26.822706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:35:34.775554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:35:42.395945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:35:50.447224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:35:58.455629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:36:06.259296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:36:14.683198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:36:22.591236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:36:30.376727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:36:38.147696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:36:45.986535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:36:54.128297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:37:02.912080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:37:11.296089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:37:23.279080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:37:32.423833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:37:40.538875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:37:48.195110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:37:56.087040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:38:05.098897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:38:13.855232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:38:23.007044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:38:30.960117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:38:39.139210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:38:47.795441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:38:56.036083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:39:04.039062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:39:12.131136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:39:21.639662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:39:29.578923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T18:39:37.924217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-12T18:43:10.880044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-12T18:43:18.675451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-12T18:43:26.523190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-12T18:43:34.626187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-12T18:39:46.285798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-12T18:39:55.104788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X0X1X2X3X4X5X6X7X8X9target
00.0321251.2134960.7176421.4396660.501081-2.5694340.879269-0.3629310.4639851.4321161
11.272424-1.4303390.974914-0.5377111.8273141.377606-1.273796-1.346682-0.7119450.5580381
20.3065990.729970-0.337485-0.092240-2.147639-0.0827181.535960-0.1243501.391558-0.0568021
3-0.222704-1.803121-2.264245-0.572824-1.836203-1.025760-0.9287781.3805240.4780040.5231340
4-0.507228-1.100418-2.090327-1.3247630.641979-0.632151-1.0746760.286795-0.4916830.2333681
50.7955710.8016490.411361-1.064584-0.944452-0.599091-0.995959-0.574758-2.3389771.0616061
61.3949870.5706360.6274622.695979-0.2289980.261691-0.7441930.0338600.002087-0.8510131
7-0.828556-0.1355520.578505-0.089624-0.102419-0.5010841.0061172.1568170.767449-0.8158860
8-0.198096-0.0946200.202847-0.466055-0.0346340.285757-0.0607711.1851380.6013120.3546401
9-1.1256441.2935040.5023410.231733-0.8637201.6076700.8723270.8227320.430643-1.0636741

Last rows

X0X1X2X3X4X5X6X7X8X9target
990-1.1265311.2414731.9102541.2674911.3787341.0575511.2221080.619360-0.9518370.6836620
9910.0835610.1062020.5681370.5793070.763009-0.219899-0.075852-0.2384990.224391-0.5485440
9921.782839-1.2002522.162560-0.921987-0.662369-0.457674-0.730031-0.5435040.037231-1.5600031
9930.776496-1.005208-0.5258941.7755590.680876-1.5842161.1377041.060009-1.2594380.4710861
9941.426953-0.5322931.849920-0.6318690.205544-0.0048351.024991-0.4990920.8958690.2252180
995-0.937318-1.0159120.011710-0.911408-0.937548-1.0271361.798208-0.3553290.735439-0.6239751
9960.364753-0.575263-0.150228-1.3734780.8897510.7616531.1627721.8032860.844454-1.9323200
997-1.147434-0.361902-1.2034720.638147-0.8486310.1052480.9759891.845959-0.0724521.3704211
998-0.404775-0.0899450.1340451.155847-1.7890680.0699940.815084-0.0602510.1649681.8853020
999-0.967015-1.738385-0.656134-1.048855-0.527655-0.501057-2.105633-0.117437-1.183225-0.1305351